1. Field of the Invention
This invention relates generally to digital video technology and more particularly to a method and apparatus for detecting unauthorized copies of video data based on the spatial and temporal content of the video data.
2. Description of the Related Art
The success of the Internet and the widespread availability of cost-effective digital storage devices have made it possible to replicate, transmit, and distribute digital content in an effortless way. Thus, the protection of Intellectual Property Rights (IPR), especially with respect to copyrights of digital images, has become a crucial legal issue. In particular, detecting copies of digital media (images, audio and video) is a basic requirement for those investigating possible copyright violations. Two applications of copy detection in use include usage tracking and copyright violation enforcement.
Currently, there are two approaches commonly used to protect a copyright of a digital image: watermarking and content-based copy detection. As is generally known, watermarking embeds information into the image prior to distribution. Thus, all copies of the marked content contain the watermark, which can be extracted, to prove ownership. For content-based copy detection additional information, beyond the image itself, is not required. Generally, the image or video contains enough unique information that can be used for detecting copies, especially illegally distributed copies. For instance, if an owner of an image or video suspects that the image is being illegally distributed on the Internet, the owner can raise a query to a copy detection system. It should be appreciated that the content-based copy detection can also be a complementary approach to watermarking. After the copy detector provides a creator or a distributor with a suspect list, the actual owner of the media can use a watermark or other authentication techniques to prove ownership.
Content-based copy detection schemes extract signatures from the original images. The same signature, extracted from the test image, is compared to the original image signature to determine if the test image is a copy of the original image. The key advantage of the content-based copy detection over watermarking is the fact that the signature extraction is not required to be conducted before the image is distributed. However, copies that are not the same as the original, i.e., copies that are slightly modified, may not be detected. For example, a third party may generate various modifications to avoid copy detection or enhance image quality which may cause the content based copy detection system not to detect the copy.
Color histogram-based methods, such as the histogram intersection method, have been used in content-based image/video retrieval systems. However, they are not suitable for copy detection systems since the color histogram does not preserve information about the spatial distribution of colors. Another method which can consider the pixel locations is the partition based approach. In this method, the image is divided into sub-images. In one such method, the color information of each partition is obtained by a local color histogram. The similarity of two images is measured by comparing their local color histogram, and by considering the similarity of all the sub-images. However, this method comes at a high computational cost and requires a long search time. Additionally, this method will not detect images that have their spatial outlay modified.
The traditional video matching techniques have predominantly relied on image correspondence. The distance between two video sequences is computed by combining dissimilarities of corresponding frames. One challenge for video matching is that the different digitizing and encoding processes give rise to several distortions, such as changes in brightness, shifts in hue, changes in saturation, different blocky artifacts, and so on. That is, most of the currently available techniques focus on coping with slight color distortions introduced by different encoding parameters. However, it should be noted that techniques to deal with aspect ratio conversions must be considered because those conversions are frequently made in practice to fit different displays. For example, the video streams may be displayed in a 4:3 or 16:9 aspect ratio. Traditional video matching techniques are not capable of accommodating the changes in aspect ratios so that copies having different aspect ratios are identified. FIGS. 1A and 1B illustrate common techniques used to change aspect ratios for video data. Video frame 100 of FIG. 1A illustrates video frame 100 in which a “letter box” approach is used to scale down the aspect ratio of a video. FIG. 1B illustrates video frame 102 in which a “pillar box” approach is used to modify the aspect ratio of a video. In addition to being unable to accommodate the aspect ratio changes, the traditional video matching techniques, e.g., key frame based approaches, correlation based methods, ordinal measuring techniques, etc., fail to consider the temporal aspects of a video stream. The failure to notice the temporal variation of videos may lead to many false detections of copies.
As a result, there is a need to solve the problems of the prior art to provide a method and apparatus for robust and efficient video copy detection where the copy's aspect ratio has been modified.